1. Field of the Invention
The present invention relates to technology for the inspection of a surface or surfaces of a workpiece, such as a semiconductor wafer, chip, or the like. More particularly, it relates to apparatus and methods for inspection of such workpiece surfaces using electromagnetic energy, e.g., light, to scan the surface to obtain characteristics of the surface or other information concerning the surface.
2. Description of the Related Art
There are a number of applications in which it is desirable or advantageous to inspect a surface or surfaces of a workpiece to obtain information about the characteristics and/or condition of that surface or surfaces. Examples of workpieces amenable to such application would include, for example, bare or unpatterned semiconductor wafers, semiconductor wafers with an applied film or films, patterned wafers, and the like. Characteristics and conditions of the surface that are commonly of interest include surface geometry such as flatness, surface roughness, etc., and/or the presence of defects, such as particles, crystal originated pits (“COPs”) and crystalline growths. Given the increasing drive over the years to reduce device size and density, there has been a need for increasing control over surface characteristics or properties at reduced dimensions, and an increasing demand for a reduction in the size of defects, the types of defects that are permissible, etc. Correspondingly, there is an enhanced need for resolution, detection and characterization of small surface characteristics, properties, defects, etc., and an enhanced need for increased measurement sensitivity and classification capability.
In the face of this demand, a number of systems and methods have emerged to provide this capability. One such system, for example, is disclosed in U.S. patent application Ser. No. 11/311,905 (the “'905 application”), which is assigned to ADE Optical Systems Corporation of Westwood, Mass. The '905 application discloses a surface inspection system and related methods for inspecting the surface of a workpiece, wherein a beam of laser light is directed to the surface of the workpiece, the light is reflected off the surface, and both scattered and specular light are collected to obtain information about the surface. An acousto-optical deflector is used to scan the beam as the wafer is moved, for example, by combined rotation and translation, so that the entire surface of the workpiece is inspected.
As our understanding of the physics and phenomenology of optical scattering from surfaces has improved, a capability has been developed and refined in which detailed and high resolution information about defects on the surface can be ascertained. These phenomena largely are obtained from the optical energy that is scattered by the surface, as opposed to the energy in the main reflected beam or the “specular beam.” Examples of systems and methods that provide such defect detection capability include not only the '905 application but also U.S. Pat. No. 5,712,701, U.S. Pat. No. 6,118,525 and U.S. Pat. No. 6,292,259, all of which are assigned to ADE Optical Systems Corporation and all of which are herein incorporated by reference as if fully set forth herein. Systems designed according to these patents have performed admirably and provided major advances over their predecessors. As the drive to smaller device dimensions and higher device densities has continued, however, the need also has continued for the ability to resolve and classify even smaller and smaller surface properties, defects, etc. A need also has developed to detect and characterize a greater range of surface characteristics and defects in terms of the types of defects, their extent or range, etc.
In the surface inspection systems mentioned above, laser light traverses a surface of a semiconductor wafer and generates reflected and scatter light. One technique for identifying a defect on the surface of a semiconductor wafer comprises developing a filtered voltage signal that is representative of the intensity of the reflected light and the scatter light, and comparing the filtered signal to a defect detection threshold. The threshold is a voltage value that represents a scatter light intensity that may be expected to be representative of a characteristic such as an actual defect. Non-zero filtered voltage signal levels less than the defect detection threshold are likely to be non-defect events, such as system noise, surface roughness, Rayleigh scatter, or other phenomenon.
A trade-off exists between the probability of defect detection and the reliability of defect detection in scattered light measurements. As the user sets the defect detection threshold lower, the surface inspection system is more likely to detect a greater number of defects, because scatter light intensity readings associated with defects having smaller sizes will be also be classified as defect events. However, as the threshold is set lower, the probability increases that an apparent defect detection actually constitutes a false identification of a defect, also known as a “false alarm”.
A defect detection threshold may be selected based on the extent of confidence that is desired in the system's defect detection capabilities. In the past, the defect detection threshold has been selected with reference to the surface inspection system's ability to reproduce its results, as reflected in a constant false alarm rate (CFAR) value. The CFAR is a constant value, constituting an expected rate of “false alarms” in the voltage signal output of the surface inspection system. At set-up of the surface inspection system, a maximum CFAR is selected to constitute the maximum probability of false defect detection that the user, designer, or operator is willing to accept. The CFAR may then be used to establish the minimum defect size that is detectable by the surface inspection system, given the selection of the CFAR as maximum acceptable probability of false defect detection.
The identification of defect candidates constitutes a forecast that the aberration being analyzed is a defect. The forecast may be simplified into a yes/no statement (categorical forecast; in this case “aberration identified as a defect candidate defect”/“not identified as defect candidate”), with the event being forecasted itself being put into one of two categories (defect event/non-defect event)
Let H denote “hits” (i.e., all correct event forecasts—an aberration identified as a defect candidate turns out to be a defect); let F denote “false alarms” (an aberration identified as a defect candidate turns out to be a non-defect event); let M denote “missed forecasts” (an aberration that had not been identified as a defect candidate turned out to be a defect event); and let Z denote correctly forecasted non-defect events. A forecast/verification table would show:
Forecast/verificationDefect eventNon-defect eventDefectH (correct forecast) (alsoF (incorrect forecast)candidate IDknown as hit)(also known asfalse alarms, falsepositive)Not DefectM (incorrect non-forecast) (alsoZ (correct non-forecast)candidateknown as false negative ormiss)
Assume altogether N forecasts with H+F+M+Z=N. In a perfect forecast, F and M are zero. The constant false alarm rate, being the fraction of defect candidates that were actually non-defect events; can be calculated using the equation: CFAR=F/N. Thus, CFAR is the fraction of false alarms in the set of all events.
Due to the physics of surface inspection systems, a threshold that has been selected with reference to the CFAR value rarely remains a constant, because a false alarm threshold is a function of the amount of light reflected and scattered from the wafer. However, the amount of light reflected and scattered from a wafer varies from wafer to wafer. Every wafer type reflects different amounts of light. Further, even the same type of wafer from different manufacturers may have considerable differences in the amount of reflected and scattered light. In a scenario in which the amount of reflected and scattered light is subject to change, use of a constant defect detection threshold will result in variation in a system's false defect detection rate, resulting in either identifying an increased number of false defects or failing to identify smaller defects that would otherwise have been reliably measurable. In summary, a lack of confidence can develop in the ability of a surface inspection system to consistently detect defects. The false alarm rate is associated with noise, where noise is defined as one standard deviation from the mean and is derived from variance as,Variance=σ2, or Noise=√{square root over (σ2)}=σ.
The false alarm rate is associated with noise, in units of sigma (σ) through the cumulative probability distribution function of a Gaussian distributed random variable. As can be seen in FIG. 15, which shows the functional relationship in graphical form, as the number of false counts decreases, the threshold normalized to the noise standard deviation increases.
False alarm thresholds adjust for the differences in wafer manufacturing process variations and measurement device variations to provide the optimal sensitivity. However, false alarm thresholds may overload the measurement system with unnecessary data when the measured wafers are not of the highest quality, such as reclaim wafers. This overload burdens the machine by slowing it down, and yields large sized wafer maps that have to be stored by the manufacturers. Additional burdens include increases in storage needs, data transport and data management.
Defect detection thresholds may also be selected based on the extent of sensitivity that is desired in the system's defect detection capabilities. In the past, the defect detection threshold has been selected with reference to the surface inspection system's sensitivity to detect aberration that could constitute defect events, as reflected in the surface inspection system's constant sensitivity (CSENS) voltage. A threshold based on the CSENS voltage is representative of the smallest defect size that is detectable given the sensitivity of the surface inspection system.
In practice, it is quite tedious to manually determine false detection rates and set optimal defect size-determined thresholds for every wafer type/batch in a production environment.